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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.16851 |
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| _version_ | 1866910026630692864 |
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| author | Mondal, Sujoy Park, Taehyuk Biswas, Sudipta Wang, Alan X. Cai, Wenshan |
| author_facet | Mondal, Sujoy Park, Taehyuk Biswas, Sudipta Wang, Alan X. Cai, Wenshan |
| contents | We introduce MxDiffusion, a hybrid physics- and data-driven diffusion-based framework that enables efficient and highly accurate generation of photonic structures from target optical properties. The improved accuracy is achieved through a two-stage generation strategy, in which the first diffusion model is explicitly trained with Maxwells equation-based loss to embed physical insight directly into the inverse design process, while the second model maps the physically consistent intermediate representation to the final structural geometry with significantly higher fidelity than solely data-driven approaches. The performance of MxDiffusion is validated on two representative applications: gold nanostructures patterned on a silica substrate and a highly tunable bandpass filter based on phase change material. In both cases, the proposed framework consistently outperforms a conventional data-driven diffusion model benchmark, particularly for out-of-training-distribution design targets and highly constrained resonance conditions. These results demonstrate the efficacy and superiority of MxDiffusion as a general physics-guided inverse design paradigm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16851 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MxDiffusion: A Physics-Aware Maxwells Law-Guided Diffusion Model Strategy for Inverse Photonic Metasurface Design Mondal, Sujoy Park, Taehyuk Biswas, Sudipta Wang, Alan X. Cai, Wenshan Optics We introduce MxDiffusion, a hybrid physics- and data-driven diffusion-based framework that enables efficient and highly accurate generation of photonic structures from target optical properties. The improved accuracy is achieved through a two-stage generation strategy, in which the first diffusion model is explicitly trained with Maxwells equation-based loss to embed physical insight directly into the inverse design process, while the second model maps the physically consistent intermediate representation to the final structural geometry with significantly higher fidelity than solely data-driven approaches. The performance of MxDiffusion is validated on two representative applications: gold nanostructures patterned on a silica substrate and a highly tunable bandpass filter based on phase change material. In both cases, the proposed framework consistently outperforms a conventional data-driven diffusion model benchmark, particularly for out-of-training-distribution design targets and highly constrained resonance conditions. These results demonstrate the efficacy and superiority of MxDiffusion as a general physics-guided inverse design paradigm. |
| title | MxDiffusion: A Physics-Aware Maxwells Law-Guided Diffusion Model Strategy for Inverse Photonic Metasurface Design |
| topic | Optics |
| url | https://arxiv.org/abs/2602.16851 |